Remote Forward Deployed Engineer: Post-Training & RL, Equity

Together; San Francisco (CA); Hybrid; USD 270,000 - 300,000; Full time

Benefits offered by this job

Startup equity

Health insurance

Remote work flexibility

Job summary

Together AI, the AI Native Cloud, seeks a Forward Deployed Engineer focused on post-training to partner with strategic customers. You will guide data strategy, experiment design, and model evaluation, shaping post-training outcomes and compute sizing for RL and fine-tuning.

Ideal candidates have 5+ years in ML engineering with hands-on post-training LLM experience, strong Python skills, and a proven ability to manage complex, customer-facing projects in a cross-functional environment.

Qualifications

• 5+ years in ML engineering or related field with post-training LLM experience.
• Expert post-training work: fine-tuning and RL for LLMs (SFT, LoRA, DPO, PPO/GRPO).
• Proven ability to design experiments and rigorous evaluations.
• Data-driven approach to building and iterating training datasets.
• Strong Python and production-grade coding skills.
• Excellent cross-functional communication and project management.

Responsibilities

• Define data strategy for model post-training and diagnose data quality issues.
• Suggest post-training approaches for each use case.
• Design controlled experiments and tracking to diagnose instabilities.
• Build evaluation frameworks with benchmarks and held-out tests.
• Guide base model selection for post-training objectives.
• Size RL and fine-tuning workloads; collaborate on capacity forecasts.
• Serve as primary post-training technical contact for strategic accounts.
• Contribute code, recipes, and reference implementations; promote new features.

Skills

Data strategy

Algorithm selection

Experiment design

Evaluation framework

Model selection

RL workload sizing

Strategic alignment

Product feedback loop

Python

Production experience

Project management

Job description

Together AI, the AI Native Cloud, seeks a Forward Deployed Engineer focused on post-training to partner with strategic customers. You will guide data strategy, experiment design, and model evaluation, shaping post-training outcomes and compute sizing for RL and fine-tuning.

Ideal candidates have 5+ years in ML engineering with hands-on post-training LLM experience, strong Python skills, and a proven ability to manage complex, customer-facing projects in a cross-functional environment.